ReviewNature reviews. Neuroscience2023
Reconstructing computational system dynamics from neural data with recurrent neural networks.
Review in Nature reviews. Neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
49 citing papers in PubMed, 101 citations in OpenAlex.
- A machine learning-based ecological momentary intervention for mental health promotion in youth: a micro-randomized trial.Translational psychiatry · 2026Trial
- Pretraining of Embodied Recurrent Networks Bridges the Gap Between Artificial and Cortical Neural Activities.Biomimetics (Basel, Switzerland) · 2026Article
- Neural population models for EEG: From Canonical models to alternative model structures.PLoS computational biology · 2026Article
- Data-driven reduced modeling of neural dynamics.Nature communications · 2026Article
- Entropy, Inhibition and Memory in Balanced Spiking Reservoirs.Entropy (Basel, Switzerland) · 2026Article
- Generative AI as a transformational logic for cognitive neuroscience.Communications biology · 2026Review
- Photonic biosensors based on nanoparticle superstructures: from data analysis to artificial intelligence (AI) detection.Fundamental research · 2026Review
- Linearizing and Forecasting: A Reservoir Computing Route to Digital Twins of the Brain.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Predictive pursuit emerges in high-dimensional recurrent neural networks.bioRxiv : the preprint server for biology · 2026Article
- Manifolds in the Medial Premotor Cortex During Switching From Attending to Tapping to a Metronome.Annals of the New York Academy of Sciences · 2026Article
- Re-engineering the disordered mind: clinical experimentation, dynamical systems, and AI for personalized psychiatry.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Review
- Universal Approximation Theorems for Dynamical Systems with Infinite-Time Horizon Guarantees.ArXiv · 2026Article
- Artificial intelligence as a surrogate brain: bridging neural dynamical models and data.National science review · 2026Review
- Endometrial immune dysregulation shapes CD8Frontiers in immunology · 2026Article
- Exploring synergies: Advancing neuroscience with machine learning.Signal processing · 2026Article
- Article
- WhenAPL bioengineering · 2025Review
- Temporal recurrence as a general mechanism to explain neural responses in the auditory system.Communications biology · 2025Article
- Manifold properties in the macaque medial premotor cortex during switching from attending to tapping to a metronome.bioRxiv : the preprint server for biology · 2025Article
- Accurate Identification of Communication Between Multiple Interacting Neural Populations.ArXiv · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational models in neuroscience usually take the form of systems of differential equations. The behaviour of such systems is the subject of dynamical systems theory. Dynamical systems theory provides a powerful mathematical toolbox for analysing neurobiological processes and has been a mainstay of computational neuroscience for decades. Recently, recurrent neural networks (RNNs) have become a popular machine learning tool for studying the non-linear dynamics of neural and behavioural processes by emulating an underlying system of differential equations. RNNs have been routinely trained on similar behavioural tasks to those used for animal subjects to generate hypotheses about the underlying computational mechanisms. By contrast, RNNs can also be trained on the measured physiological and behavioural data, thereby directly inheriting their temporal and geometrical properties. In this way they become a formal surrogate for the experimentally probed system that can be further analysed, perturbed and simulated. This powerful approach is called dynamical system reconstruction. In this Perspective, we focus on recent trends in artificial intelligence and machine learning in this exciting and rapidly expanding field, which may be less well known in neuroscience. We discuss formal prerequisites, different model architectures and training approaches for RNN-based dynamical system reconstructions, ways to evaluate and validate model performance, how to interpret trained models in a neuroscience context, and current challenges.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.